Exchange rate and sentiment: is there a connection?

Before we can start with our machine learning model we need to understand the relationship between the two variables, therefore we should calculate covariance. This measures the direction of a relationship between the two variables.

Loading libraries to be used

library(tidyverse)
library(writexl)
library(plyr)
library(lubridate)
library(plotly)
library(dplyr)
library(corrplot)
library(formatR)

First step: creating a dataframe from the csv

btc_exchange_rate_history <- read.csv("D:/Suli/Szakdolgozat1/development_n_stuff/aggregated_data.csv") %>%
    select(-X) %>%
    mutate(created_at = as_date(created_at))



btc_usd_tweets_combined <- read.csv("D:/Suli/Szakdolgozat1/data_to_be_cleaned/coin_Bitcoin.csv") %>%
    mutate(Date = as_date(Date)) %>%
    mutate(year = year(Date), month = month(Date), day = day(Date)) %>%
    mutate(Date = make_date(year, month, day)) %>%
    mutate(xchange_rate_change = Close - Open) %>%
    filter(Date >= "2019-01-01" & Date <= "2022-03-30") %>%
    inner_join(btc_exchange_rate_history, by = c(Date = "created_at")) %>%
    select(c(-year, -month, -day))

Second step: plotting exchange rate change and average sentiment for further insights

btc_sent_lineplot <- plot_ly(data = btc_usd_tweets_combined, x = ~Date, y = ~xchange_rate_change,
    name = "BTC change", type = "scatter", mode = "lines", color = "red") %>%
    add_trace(data = btc_usd_tweets_combined, x = ~Date, y = ~daily_avg_sent, yaxis = "y2",
        name = "Avg. sentiment", mode = "lines", color = "blue") %>%
    layout(title = "Bitcoin exchange rate change compared to previous day's closing rate",
        margin = list(t = 150), legend = list(x = 1.1), paper_bgcolor = "rgb(255, 255, 255)",
        plot_bgcolor = "rgb(255, 255, 255)", xaxis = list(title = "Date", range = list("2019-01-01 00:00:00",
            "2019-12-31 23:59:59"), rangeslider = list(type = "date", visible = T),
            list(dtickrange = list(NULL, 1000), value = "%H:%M:%S.%L ms"), list(dtickrange = list(1000,
                60000), value = "%H:%M:%S s"), list(dtickrange = list(60000, 3600000),
                value = "%H:%M m"), list(dtickrange = list(3600000, 86400000), value = "%H:%M h"),
            list(dtickrange = list(86400000, 604800000), value = "%e. %b d"), list(dtickrange = list(604800000,
                "M1"), value = "%e. %b w"), list(dtickrange = list("M1", "M12"),
                value = "%b '%y M"), list(dtickrange = list("M12", NULL), value = "%Y Y"),
            rangeselector = list(buttons = list(list(count = 1, label = "1M", step = "month",
                stepmode = "backward"), list(count = 6, label = "6M", step = "month",
                stepmode = "backward"), list(count = 1, label = "1Y", step = "year",
                stepmode = "backward"), list(count = 1, label = "YTD", step = "year",
                stepmode = "todate"), list(step = "all", label = "ALL"))), list(dtick = "M1",
                tickformat = "%b\n%Y", ticklabelmode = "period")), yaxis = list(title = "BTC exchange rate change",
            range = c(min(btc_usd_tweets_combined$xchange_rate_change), max(btc_usd_tweets_combined$xchange_rate_change)),
            gridcolor = "rgb(255,255,255)", showgrid = TRUE, showline = FALSE, showticklabels = TRUE,
            tickcolor = "rgb(140, 140, 140)", ticks = "outside", zeroline = FALSE),
        yaxis2 = list(title = "Daily average sentiment", overlaying = "y", side = "right",
            range = c(min(btc_usd_tweets_combined$daily_avg_sent), max(btc_usd_tweets_combined$daily_avg_sent))))


btc_sent_lineplot

Third step: plotting the data on a scatterplot

todo: exchange price changehez nézni, nem az árhoz

btc_sent_scatterplot <- plot_ly(data = btc_usd_tweets_combined, y = ~daily_avg_sent,
    x = ~xchange_rate_change, marker = list(size = 4, color = "rgba(255, 182, 193, .9)",
        line = list(color = "rgba(152, 0, 0, .8)", width = 1))) %>%
    layout(yaxis = list(title = "Daily average sentiment"), xaxis = list(title = "BTC exchange rate change"))

btc_sent_scatterplot

Fourth step: calculate covariance and correlation

btc_cov <- cov(btc_usd_tweets_combined$daily_avg_sent, btc_usd_tweets_combined$xchange_rate_change,
    method = "pearson")

btc_cov

## [1] 3.301177

A positive covariance means that the two variables tend to increase or decrease together. Correlation helps us analyze the effect of changes made in one variable over the other variable of the dataset. Now that we know this, we should calculate the strength of the relationship between two, numerically measured, continuous variables.

btc_cor <- cor(btc_usd_tweets_combined$daily_avg_sent, btc_usd_tweets_combined$xchange_rate_change,
    method = "pearson")

btc_cor

## [1] 0.1162632

One of the most common ways to quantify a relationship between two variables is to use the Pearson correlation coefficient, which is a measure of the linear association between two variables.

It always takes on a value between -1 and 1 where:

-1 indicates a perfectly negative linear correlation between two variables

0 indicates no linear correlation between two variables

1 indicates a perfectly positive linear correlation between two variables

Often denoted as r, this number helps us understand the strength of the relationship between two variables. The closer r is to zero, the weaker the relationship between the two variables.

A weak correlation indicates that there is minimal relationship between the variables.